Does nutrition knowledge training improve dietary diversity and nutrition intake? Insights from rural China

Abstract

Improving dietary diversity and nutrition intake among rural households is essential to promote sustainable rural development. In this study, we examine whether nutrition knowledge training increases rural households' dietary diversity, nutrition intake, and total calorie intake. We consider three macronutrients (proteins, fats, and carbohydrates) and three micronutrients (vitamin C, iron, and zinc) to capture nutrition intake. The doubly robust inverse probability weighted regression adjustment estimator is employed to estimate survey data of 765 rural households collected by the Institute of Food and Nutrition Development, Ministry of Agriculture and Rural Affairs of China. The results show that nutrition knowledge training enhances household dietary diversity, improves the intake of macronutrients in terms of proteins and fats and micronutrients in terms of zinc, and increases total calorie intake. In addition, the intake of nutrients and calories of males responds to nutrition knowledge training while that of females does not. The dietary diversity of females increases with nutrition knowledge training, whereas that of males is unaffected. {EconLit Citations: I12, I18, Q12, E24}.

Does access to financial services improve nutritional intake among rural residents? Assessing potential action mechanism pathways

“…Therefore, we used the endogenous treatment Poisson regression (ETPR) model to solve this study's access to FS endogeneity issue to ensure consistent and reliable estimations. Due to its benefits over other econometric models like the regression adjustment (RA) estimator, propensity score matching (PSM), inverse probability‐weighted with regression adjustment (IPWRA), and inverse‐probability weighting (IPW) estimator, the ETPR model has been used in prior research Asante et al (2022), Ma and Wang (2020), Ma, Grafton, et al (2020), Ma, Nie, et al (2020), Zheng et al (2021) and Zheng, Ma, et al (2023), and Zheng, Vatsa, et al (2023) to resolve the binary treatment endogenous variable. The ETPR model effectively compensates for biases coming from both unobserved and observed factors, in contrast to the other models (PSM, RA, IPW, and IPWRA), which may still result in biased results since they are unable to address partial selection from unseen factors (Ankrah Twumasi, Jiang, Asante, et al, 2021; Miyata et al, 2009).…”

“…Food and nutrition security, which has been defined by FAO as “when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active healthy life” has a positive relationship with household welfare (FAO, IFAD, UNICEF, WFP, & WHO, 2021; Timmer, 2010). For example, attributes of household welfare, including good health, high productivity and wage earnings, and good outcomes of education engagement can be attained when food and nutrition security improves (Gundersen & Ziliak, 2015; Ma et al, 2022; Mishra & Rampal, 2020; Weaver et al, 2021; Zheng, Ma, et al, 2023). Also, available studies show that inadequate food and nutritional intake are directly related to national and household financial environment, with patterns mirroring those of unemployment, destitution, and prices of food (Koomson, Villano, et al, 2022; Niles et al, 2020).…”